The forecast of wind resources and so to forecast a wind-farm output-power will improve the ability, to commit specific power-production from a wind farm.
Consequently, an accurate forecast is needed to improve the value of wind power. This could pave the way for a higher penetration with wind-farms, too, than it is now.
There are numbers of concepts to forecast the wind-resources and the output-power of a wind-farm. They are based on a traditional numerical weather prediction by tools at a mesoscale level—“Mesoscale Meteorology” is the study of weather systems, which are smaller than synoptic scale systems but larger than microscale and storm-scale cumulus systems.
However experience shows that the accuracy of this kind of models is not sufficient for power-output commitments that could be associated with high penalties.
The U.S. Pat. No. 7,228,235 discloses an enhanced method for the forecast, where public available long-term data-sets of locations are considered, which are near to a planned wind-farm location. A test-tower is located at the potential new location being used to collect more short-term data-sets. The long-term data sets and the short-term data-sets are combined to be used by a computer-learning-system. So it is possible to predict Long-term data-sets for the potential wind-farm location.